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Practicing Machine Learning Interview Questions in Python

Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.

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4 Hours16 Videos60 Exercises
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Course Description

Prepare for Your Machine Learning Interview

Have you ever wondered how to properly prepare for a Machine Learning Interview? In this course, you will prepare answers for 15 common Machine Learning (ML) in Python interview questions for a data scientist role.

These questions will revolve around seven important topics: data preprocessing, data visualization, supervised learning, unsupervised learning, model ensembling, model selection, and model evaluation.

Refresh Your Machine Learning Knowledge

You’ll start by working on data pre-processing and data visualization questions. After performing all the preprocessing steps, you’ll create a predictive ML model to hone your practical skills.

Next, you’ll cover some supervised learning techniques before moving on to unsupervised learning. Depending on the role, you’ll likely cover both topics in your machine learning interview.

Finally, you’ll finish by covering model selection and evaluation, looking at how to evaluate performance for model generalization, and look at various techniques as you build an ensemble model.

Practice Answers to the Most Common Machine Learning Interview Questions

By the end of the course, you will possess both the required theoretical background and the ability to develop Python code to successfully answer these 15 questions.

The coding examples will be mainly based on the scikit-learn package, given its ease of use and ability to cover the most important machine learning techniques in the Python language.

The course does not teach machine learning fundamentals, as these are covered in the course's prerequisites.
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  1. 1

    Data Pre-processing and Visualization

    Free

    In the first chapter of this course, you'll perform all the preprocessing steps required to create a predictive machine learning model, including what to do with missing values, outliers, and how to normalize your dataset.

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    Handling missing data
    50 xp
    The hunt for missing values
    100 xp
    Simple imputation
    100 xp
    Iterative imputation
    100 xp
    Data distributions and transformations
    50 xp
    Training vs test set distributions and transformations
    50 xp
    Train/test distributions
    100 xp
    Log and power transformations
    100 xp
    Data outliers and scaling
    50 xp
    Outlier detection
    100 xp
    Handling outliers
    100 xp
    Z-score standardization
    100 xp
  2. 2

    Supervised Learning

    In the second chapter of this course, you'll practice different several aspects of supervised machine learning techniques, such as selecting the optimal feature subset, regularization to avoid model overfitting, feature engineering, and ensemble models to address the so-called bias-variance trade-off.

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Datasets

DiabetesLoans datasetLoans training set (reduced)

Collaborators

Collaborator's avatar
Adel Nehme
Lisa Stuart HeadshotLisa Stuart

Data Scientist

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